Big data-based grey forecast mathematical model to evaluate the effect of Escherichia coli infection on patients with lupus nephritis
| Autor: | Yansheng Jin, Lan Ding, Shuaishuai Gu, Ahmed Mohamed Hamad Arbab, Eman Ghonaem, Maoxiao Fan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
medicine.drug_class QC1-999 Antibiotics Lupus nephritis General Physics and Astronomy 02 engineering and technology Logistic regression 01 natural sciences Procalcitonin Internal medicine 0103 physical sciences Escherichia coli Medicine Escherichia coli infection 010302 applied physics Related factors business.industry Physics Grey theory prediction Bio-marker 021001 nanoscience & nanotechnology medicine.disease Gram-negative bacteria 0210 nano-technology business |
| Zdroj: | Results in Physics, Vol 26, Iss, Pp 104339-(2021) |
| ISSN: | 2211-3797 |
| Popis: | The grey predictive mathematical model based on big data was used for analysis on the effect of Escherichia coli infection on patients with lupus nephritis (LN) in this study. Then, 156 patients diagnosed with LN infections by Wuzhong People’s Hospital’s information system (HIS) from October 30, 2017 to October 30, 2019 were selected as the experimental group, and 89 patients without LN infections were selected as the control group. Besides, the grey theory mathematical model was applied to process the integrated data, and feature analysis was employed to screen out disease-related bio-markers for the diagnosis of LN. The two groups were compared for affected organs, treatment, laboratory indicators, pathogenic bacteria, and recovery status. Multivariate logistic regression was used to analyze the related factors of patients with infections. The results showed that the specificity, sensitivity, and accuracy of the big data diagnosis based on the grey theory mathematical model were 78.9%, 87.6%, and 92.1, respectively; hormones, c-reactive protein, procalcitonin, and the daily antibiotic dose were positively correlated with concurrent infections (P |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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